Tehran driving cycle development using the k-means clustering method

This paper describes the development of a car driving cycle for the city of tehran and its suburbs using a new approach based on driving data clustering. in this study, driving data gathering is performed under real traffic conditions using advanced vehicle location (avl) devices installed on private cars. the recorded driving data is then analyzed, based on ‘‘micro-trip’’ definition. two driving features including ‘‘average speed’’ and ‘‘idle time percentage’’ are calculated for all micro-trips. the micro-trips are then clustered into four groups in driving feature space using the k-means clustering method. for development of the driving cycle, the nearest micro-trips to the cluster centers are selected as representative microtrips. the new method for driving cycle development needs less computation compared to the sapm method. in addition, it benefits the capability of the k-means clustering method for traffic condition grouping. the developed driving cycle contains a 1533 s speed time series, with an average speed of 33.83 km/h and a distance of 14.41 km. finally, the characteristics of the developed driving cycle are compared with some other light vehicle driving cycles used in other countries, including ftp-75, ece, eudc and j10-15 mode.